TR2020-166
Spatio- Temporal Graph Scattering Transform
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- "Spatio- Temporal Graph Scattering Transform", IEEE Transactions on Pattern Analysis and Machine Intelligence, December 2020.BibTeX TR2020-166 PDF
- @article{Chen2020dec,
- author = {Chen, Siheng and Li, Maosen and Chen, Xu and Zhang, Ya and Wang, Yanfeng and Tian, Qi},
- title = {Spatio- Temporal Graph Scattering Transform},
- journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-166}
- }
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- "Spatio- Temporal Graph Scattering Transform", IEEE Transactions on Pattern Analysis and Machine Intelligence, December 2020.
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Research Areas:
Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. Furthermore, spatio-temporal graph neural networks lack theoretical interpretation. To address these issues, we put forth a novel mathematically designed framework to analyze spatio-temporal data. Our proposed spatio-temporal graph scattering transform (ST-GST) extends traditional scattering transforms to the spatiotemporal domain. It performs iterative applications of spatio-temporal graph wavelets and nonlinear activation functions, which can be viewed as a forward pass of spatio-temporal graph convolutional networks without training. Since all the filter coefficients in ST-GST are mathematically designed, it is promising for the real-world scenarios with limited training data, and also allows for a theoretical analysis, which shows that the proposed ST-GST is stable to small perturbations of input signals and structures. Finally, our experiments show that i) ST-GST outperforms spatio-temporal graph convolutional networks by an increase of 35% in accuracy for MSR Action3D dataset; ii) it is better and computationally more efficient to design the transform based on separable spatio-temporal graphs than the joint ones; and iii) the nonlinearity in ST-GST is critical to empirical performance.